Do Indian Researchers Hedge Less? We Counted 8,000 Abstracts (2026)

Indian research abstracts hedge at essentially the same rate as the rest of the world’s and use boosters 25% more often — and almost the entire gap is in claims about how important the topic is, not in claims about what the evidence showed.

That is the finding from 8,000 abstracts of journal articles published in 2025, retrieved from the OpenAlex API on 23 August 2026: 4,000 with at least one Indian institutional affiliation and 4,000 drawn from the same population without a country filter. Every figure below is recomputed from those abstracts, and the lexicons and the code path are described so you can disagree with them.

The headline numbers

The first thing to note is what the data does not show. The common assumption — that researchers writing in English as an additional language hedge too little and state findings too flatly — is not visible here. Indian abstracts hedge slightly more than the comparison sample, not less, and a slightly higher proportion of them contain at least one hedge.

The difference is entirely on the other side of the ledger. Indian abstracts carry 4.57 boosters per 1,000 words against 3.67 in the world sample, a 24.5% higher rate, and the share of abstracts containing at least one booster is 53.3% against 40.5%.

Where the gap actually sits

Splitting the boosting vocabulary into families locates almost all of it. These family lists are defined separately from the headline booster list above and overlap it only in part, so their rates do not sum to it.

Read that table one row at a time, because the story is in the contrast between the first two.

Evidential boosters are level. Indian abstracts say demonstrated, confirmed and established at very nearly the same rate as everyone else — 1.77 against 1.67 per 1,000 words, a difference of about one word in every ten abstracts. On the claims that actually attach to data, Indian research writing is calibrated the same way the rest of the literature is.

Importance boosters are not level. They run 49% higher. The proportion of abstracts containing at least one importance word is 27.3% in the Indian sample against 17.6% in the world sample.

Because critical is genuinely ambiguous — critical temperature, critical path, critical care, critical race theory — the same figures were recomputed with it removed. The gap widens rather than closing: essential, crucial and vital alone run 1.15 per 1,000 words in the Indian sample against 0.69 in the world sample, a difference of 67%.

The individual words

The three most frequent hedges are the same in both samples and in the same order: potential, may and often. In the Indian sample potential alone accounts for 1,040 of 3,684 hedge tokens.

The most frequent boosters diverge. The Indian sample’s top three are essential (404 occurrences), crucial (371) and demonstrated (342). The world sample’s top three are novel (288), demonstrated (274) and demonstrate (233). Two importance words lead one list; two evidence words lead the other.

What was counted, and how

Transparency here matters more than usual, because a hedging count is only as good as its word list.

Hedges (43 forms counted): may, might, could, appear(s/ed), seem(s/ed), suggest(s/ed), possible, possibly, probable, probably, likely, unlikely, potential, potentially, perhaps, relatively, somewhat, apparently, presumably, assume(d), tend(s/ed), approximately, largely, generally, typically, usually, often, arguably, plausible, plausibly, indicative, broadly, partially, seemingly.

Boosters (41 forms counted): demonstrate(s/d), prove(s/d/n), clearly, strongly, definitely, undoubtedly, certainly, obviously, evidently, must, always, never, highly, substantially, considerably, remarkable, remarkably, novel, unprecedented, superior, excellent, outstanding, establish(es/ed), confirm(s/ed), conclusively, undeniably, markedly, greatly, tremendous, vital, crucial, essential.

Matching is on whole lowercase word forms after stripping punctuation, so no substring noise. Rates are per 1,000 words of abstract text, which controls for the small difference in abstract length between the two samples.

“Significant” was deliberately excluded from the booster list. In a results abstract it is usually a statistical term with a precise meaning, not a rhetorical intensifier, and counting it as a booster would inflate every scientific corpus at once. It is reported separately in the headline table, where the Indian rate is 18% higher.

What this measurement does not establish

Four limits, stated plainly.

• Affiliation is not authorship geography. The Indian sample is defined by at least one Indian institutional affiliation on the record, which includes internationally co-authored papers and excludes Indian researchers working abroad. It is a reasonable proxy and not the same thing.
• Abstracts are not theses. An abstract is the most compressed and most heavily edited text a researcher produces. The pattern found here may be stronger or weaker in a thesis chapter, and this study cannot say which.
• Field composition differs between the samples. No attempt was made to match the two samples by discipline, and disciplines differ in their baseline use of both families. A field-matched comparison would be a stronger design and is not what was run.
• A rate is not a judgement. Nothing here shows that the Indian rate is wrong and the world rate right. What it shows is a measurable difference in one family of words, which matters mainly because international reviewers and examiners read against the second distribution.

The wider caution about counting Indian research output — and why two honest sources give different totals for the same year — is set out in our analysis of how many research papers India publishes, which draws on the same database.

What it means for your own thesis

The actionable version is short: your evidence verbs are probably fine, and your importance words probably are not.

An importance claim is unfalsifiable. Nobody can check whether a topic is crucial, which is exactly why an examiner discounts the sentence — and why a paragraph that opens on one starts from zero credibility. The replacement is not a weaker word; it is a fact.

The pattern is the same in every row: a number, a comparison or an absence replaces an adjective. That is also the sentence an examiner cannot argue with.

A ten-minute check on your own abstract

1. Search the abstract for seven words: essential, crucial, vital, critical, imperative, indispensable, paramount. In 195 words — the median length in this sample — you should expect roughly zero to one.
2. For each hit, ask what fact it is standing in for. Write the fact instead. If there is no fact, the sentence was doing no work.
3. Then search for the evidence family: demonstrates, proves, establishes, confirms, clearly. Keep the ones your design actually licenses and drop the rest a rung — which verb sits on which rung is set out in our verb bank for reporting verbs in an Indian thesis.
4. Count your hedges. Zero hedges in a whole abstract is as much a signal as five importance words, and it usually means a finding has been stated more strongly than the design allows.
5. Repeat on your conclusion section only. Abstracts and conclusions carry the overwhelming majority of both families, and they are the two sections most readers actually read.

Claim strength inside the discussion chapter is a separate craft with its own rules — bounding each finding, handling non-significant results and writing limitations that strengthen rather than apologise — and that is worked through in our guide to turning results into argument. The register questions that sit alongside all of this, from “as per” to the emphatic particles, are in our guide to Indian English in a PhD thesis. And if the abstract you are auditing is destined for a journal rather than an examiner, the compression rules differ again, as our guide to turning a thesis into a journal paper sets out.

Writing at the strength your data licenses

Calibration is hard to do to your own writing, because the sentence that overclaims felt true when you wrote it. It is much easier when the claim sits next to the evidence it rests on rather than in a different file.

Tesify builds your chapters with every claim attached to its source, so the gap between what a study found and how strongly you have stated it is visible while you write rather than at the viva. The judgement about how far to push a finding stays yours.

Write your thesis with Tesify

Frequently asked questions

What is a hedge in academic writing?

A word or construction that limits how strongly a claim is made — may, might, suggests, likely, potentially, appears to. Hedging is not weakness; it is the mechanism by which a writer states exactly as much as the evidence supports and no more.

What is a booster?

The opposite move: a word that strengthens a claim beyond its plain statement — demonstrates, clearly, undoubtedly, crucial, essential. Boosters divide into evidence claims, which a study can support, and importance claims, which nothing can verify.

Do Indian researchers hedge less than researchers elsewhere?

Not in this sample. Indian-affiliated abstracts carried 4.57 hedges per 1,000 words against 4.34 in the comparison sample, and a slightly higher share of them contained at least one hedge. The measurable difference is in boosters, not hedges.

How much bigger is the booster gap?

Boosters run at 4.57 per 1,000 words in Indian-affiliated abstracts against 3.67 in the world sample, a rate 24.5% higher. Broken down by family, importance boosters are 49% higher while evidential boosters are only 6% higher.

Which words drive the difference?

Essential, crucial and vital. Counted together and excluding the ambiguous word critical, they occur at 1.15 per 1,000 words in the Indian sample against 0.69 in the world sample, a difference of 67%.

Why was “significant” left out of the booster count?

Because in a results abstract it usually carries a precise statistical meaning rather than a rhetorical one, and counting it would inflate every scientific corpus at once. It is reported on its own line instead, where the Indian rate is 18% higher.

Where does the data come from?

The OpenAlex API, filtered to journal articles published in 2025 with an abstract present, sampled twice with different random seeds and pooled. The Indian sample additionally required at least one Indian institutional affiliation. Retrieved 23 August 2026.

How stable are these numbers?

They were computed twice on independent random samples of 2,000 abstracts each before pooling. The two runs agreed closely on every headline measure — for example the importance-booster rate came out at 1.87 and 1.79 per 1,000 words in the two Indian samples.

Does this mean my thesis will be marked down for saying “crucial”?

No. One importance word costs nothing. What costs is a pattern — an abstract and an introduction built on unverifiable adjectives instead of on figures, which is what makes a reader discount the paragraphs that follow.

How many hedges should an abstract contain?

There is no correct number, but the median abstract in this sample runs 195 words and carries roughly one hedge. Zero across an entire abstract is worth checking, because it usually means a finding has been stated more firmly than the design allows.

Can I reuse these figures in my own literature review?

Yes, with attribution to this page and the retrieval date, and read the limitations section first — particularly that affiliation is not the same as author nationality and that the two samples were not matched by discipline.